Abstract: Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.

Abstract: A processing system may obtain a feature set for segmenting households comprising subscribers of a telecommunication network into segments, the households including reporting households for which an information value regarding a feature of interest is available, and non-reporting households for which an information value regarding the feature of interest is not available. The processing system may then assign the households to segments, each segment associated with a set of information values for features of the feature set, and where for each segment, households assigned to the segment have information values that are the same for each of the features of the feature set. The processing system may also link each non-reporting household in a segment to a reporting household in the segment. The processing system may then reconfigure the telecommunication network in accordance with information values for the at least one feature of interest for the plurality of households.

Abstract: Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.

Abstract: A processing system may obtain a feature set for segmenting households comprising subscribers of a telecommunication network into segments, the households including reporting households for which an information value regarding a feature of interest is available, and non-reporting households for which an information value regarding the feature of interest is not available. The processing system may then assign the households to segments, each segment associated with a set of information values for features of the feature set, and where for each segment, households assigned to the segment have information values that are the same for each of the features of the feature set. The processing system may also link each non-reporting household in a segment to a reporting household in the segment. The processing system may then reconfigure the telecommunication network in accordance with information values for the at least one feature of interest for the plurality of households.

Abstract: A processing system may obtain a feature set for segmenting households comprising subscribers of a telecommunication network into segments, the households including reporting households for which an information value regarding a feature of interest is available, and non-reporting households for which an information value regarding the feature of interest is not available. The processing system may then assign the households to segments, each segment associated with a set of information values for features of the feature set, and where for each segment, households assigned to the segment have information values that are the same for each of the features of the feature set. The processing system may also link each non-reporting household in a segment to a reporting household in the segment. The processing system may then reconfigure the telecommunication network in accordance with information values for the at least one feature of interest for the plurality of households.